Identification of Sparse Volterra Systems: An Almost Orthogonal Matching Pursuit Approach
Changming Cheng, Er‐Wei Bai, Zhike Peng
Abstract
This paper considers identification of sparse Volterra systems. A method based on the almost orthogonal matching pursuit (AOMP) is proposed. The AOMP algorithm allows one to estimate one non-zero coefficient at a time until all non-zero coefficients are found without losing the optimality and the sparsity, thus avoiding the curse of dimensionality often encountered in Volterra system identification.
Topics & Concepts
Matching pursuitIdentification (biology)Matching (statistics)Computer scienceControl theory (sociology)System identificationMathematicsMathematical optimizationAlgorithmArtificial intelligenceControl (management)Data modelingStatisticsCompressed sensingBiologyBotanyDatabaseControl Systems and IdentificationStructural Health Monitoring TechniquesAdvanced Adaptive Filtering Techniques